# Mathematical Statistics @ UNIPI (2023-2024)

Class times: Aula N, Polo Fibonacci, UNIPI,
Monday 14:00 - 16:00,
Thursday 09:00 - 11:00.

Class format: In-person

Instructors: Andrea Agazzi, Katerina Papagiannouli,

E-mail: andrea.agazzi at unipi.it (please include "MathStat" in email title).

Corse summary:
Data are everywhere, but how can we extract information from those data on the reality that has produced them? This class discusses the mathematical foundations of the techiques used to analyze data. More specifically, we will discuss in depth the design and performance of various tools to perform statistical inference. The course is designed to equip students with the skills to apply statistical methods to real-world problems.
Class requirements: Elementi di Probabilita e statistica or equivalent.

Textbooks and references (to be updated during the course):

• Asymptotic Statistics A. W. van der Vaart (Cambridge University press),
• Lecture notes on Mathematical Statistics S. Van de Geer (available online),
• Lecture notes on Mathematical Statistics M. Pratelli and R. Giuliano (available online),

Lecture notes:
26.02.2024 Lecture 1
29.02.2024 Lecture 2
04.03.2024 Lecture 3
07.03.2024 Lecture 4
11.03.2024 Lecture 5
14.03.2024 Lecture 6
18.03.2024 Lecture 7
21.03.2024 Lecture 8
25.03.2024 Lecture 9
28.03.2024 Lecture 10
08.04.2024 Lecture 11
11.04.2024 Lecture 12
15.04.2024 Lecture 13
18.04.2024 Lecture 14
30.04.2024 Lecture 15
02.05.2024 Lecture 16
06.05.2024 Lecture 17
09.05.2024 Lecture 18
16.05.2024 Lecture 19
20.05.2024 Lecture 20
23.05.2024 Lecture 21

Homework exercises:
Homework 1 (presented 18.03.2024, solution sketch)
Homework 2 (presented 25.03.2024, solution sketch)
Homework 3 (presented 08.04.2024, solution sketch)
Homework 4 (presented 15.04.2024, solution sketch)
Homework 5 (presented 19.04.2024, solution sketch)
Homework 6
Homework 7
Homework 8
Homework 9 (not presented, solution sketch)

Midterm-like exercises:
Midterm-like exercises